Journal ArticleDOI
Vehicle Detection Based on Semantic-Context Enhancement for High-Resolution SAR Images in Complex Background
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TLDR
A vehicle detector named SCEDet for the small-scale vehicles in SAR images is proposed to improve the detection performance and the speed, and the experimental results on the FARAD dataset demonstrate that both the detectionPerformance and thespeed are much better than other detection methods under the same hardware conditions.Abstract:
Small-scale target detection (such as vehicles) in complex synthetic aperture radar (SAR) image scenes has always been a pain point for the advanced convolutional neural network (CNN)-based target detectors because of the downsampling operations and the local receptive field characteristics of CNNs. To tackle these limitations, a vehicle detector named SCEDet for the small-scale vehicles in SAR images is proposed to improve the detection performance in this letter. SCEDet mainly consists of two parts: subaperture semantic feature extraction and subaperture semantic-context enhancement (SCE) with SCE module. First, ResNet34 with subaperture decomposition is used to efficiently exploit the latent subaperture semantic features. Then, the SCE module is proposed to balance the multiscale semantic information as well as aggregate the global context information for vehicle detection with a small number of parameters and computation costs. The experimental results on the FARAD dataset (0.1 m $\times0.1$ m, Ka-band) demonstrate that both the detection performance and the speed are much better than other detection methods under the same hardware conditions.read more
Citations
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A High-Precision Vehicle Detection and Tracking Method Based on the Attention Mechanism
TL;DR: Zhang et al. as mentioned in this paper proposed a novel vehicle detection and tracking method for small target vehicles based on the attention mechanism, where the feature extraction process is embedded in the prediction head for joint training.
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Object-Oriented Change Detection Method Based on Spectral-Spatial-Saliency Change Information and Fuzzy Integral Decision Fusion for HR Remote Sensing Images
TL;DR: In this article , an object-oriented change detection approach is proposed which integrates spectral-spatial-saliency change information and fuzzy integral decision fusion for high-resolution remote sensing images with the purpose of eliminating the impact of detection noise.
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A Feature Decomposition-Based Method for Automatic Ship Detection Crossing Different Satellite SAR Images
TL;DR: It is argued that the local and global features extracted from source domain and target domain contain domain-specific features (DSF) for adversarial DA and DIFs that contribute to object regression localization in the detection task.
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Self-Supervised SAR Image Registration With SAR-Superpoint and Transformation Aggregation
TL;DR: In this article , an efficient self-supervised deep learning registration network for multitemporal SAR image registration, SAR-superpoint and transformation aggregation network (SSTA-Net), is proposed.
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Small-Scale Ship Detection for SAR Remote Sensing Images Based on Coordinate-Aware Mixed Attention and Spatial Semantic Joint Context
TL;DR: Zhang et al. as discussed by the authors proposed a coordinate-aware mixed attention mechanism and spatial semantic joint context method to enhance the feature expression and distinctiveness of small-scale ship objects.
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